The following is returned from SYSTEM$CLUSTERING_INFORMATION() for a table named ORDERS with a DATE column named O_ORDERDATE: What does the total_constant_partition_count value indicate about this…

Question

Exhibit

Options

• AThe table is clustered very well on O_ORDERDATE, as there are 493 micro-partitions that could
• BThe table is not clustered well on O_ORDERDATE, as there are 493 micro-partitions where the
• CThe data in O_ORDERDATE does not change very often, as there are 493 micro-partitions
• DThe data in O_ORDERDATE has a very low cardinality, as there are 493 micro-partitions where

• A
  89% (34)
• B
  3% (1)
• C
  3% (1)
• D
  5% (2)

Explanation

Option A is correct because total_constant_partition_count counts micro-partitions that contain only one distinct value for the clustering key - meaning every row in those 493 partitions shares the same O_ORDERDATE value. This is the definition of ideal clustering: Snowflake can perfectly prune those partitions during a date-range query (fully include or fully exclude them), which is the goal of clustering.

Why the distractors are wrong:

• B inverts the logic - a high constant partition count signals good clustering, not poor clustering.
• C confuses clustering metadata with DML frequency; total_constant_partition_count reflects value distribution within partitions, not how often data changes.
• D conflates partition-level uniformity with column cardinality; a date column can have high cardinality overall and still produce constant partitions if the data is well-sorted - in fact, that's the goal.

Memory tip: Think of "constant" as "one-note" - a micro-partition singing only one date value is perfectly prunable. More one-note partitions = better clustering depth = faster queries.

No community discussion yet for this question.